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Chapter 7
Polar, Bode,and APHT Plots
from every data sample of the uncompensated data (blue) to produce the compensated data (green). The compensated phase plot is slightly noi sier near the compensation speed, wh ere the amplitude is low. Note that the zero amplitude point on the Bode amplitude plot (red circle) corresponds to the slow roll vector speed. On the polar plot, slow roll compensation is equivalent to shifting the entire plot by the inverse of the slow roll vector (the small red arrow), or by moving the origin over to the slow roll sample point. This will place the compensation speed point at the origin of the polar plot, as seen in the figure. This technique allows polar plots to be easily compensated by inspection. Notice that the shape of the polar plot does not change. It is much more difficult to compensate a Bode plot by inspection. Information Contained in Polar and Bode Plots
Polar and Bode plots are an important window to the dynamic behavior of rotor systems. They are used primarily for two purposes, balancing and the identification and analysis of resonances. Thus, it is difficult to discuss the use of polar and Bode plots without touching on some rotor dynamic theory, a large and complex topic that will be presented later in this book. Because of space limitations, the rotor dynamic discussion here will be very brief. Balancing, which is a complex subset of rotor dynamic theory, is covered in Chapter 16. Basic rotor dynamic theory predicts that, for synchronous (I X) behavior, at speeds well below a resonance, the he avy spot (which represents concentrated rotor unbalance) and the high spot will be in phase (0 phase lag). Thus, lowspeed vibration vectors (which represent the location of the high spot when the Keyphasor event occurs) will point in the direction of the heavy spot. At the resonance, the amplitude will reach a maximum, and the high spot will lag th e heavy spot by about 90 In other words, the vibration vector at resonance will point about 90 (measured in a direction opposite rotation) from the direction of the hea vy spot. At speeds well above a resonance, the amplitude will decline to a constant value, and the high spot will lag the heavy spot by about 180 the vibration vectors at high speed will point in the opposite direction from the heavy spot. See Chapter 11 for a detailed development of these principles. These basic rotor behavior characteristics are used to extract information from polar and Bode plots. In th e most common application, balancing, the IX compensated plots are used to identify resonances, determine the mode shapes of the resonances, and identify the location ofthe heavy spot(s) for each mode. IX, compensated polar and Bode plots can also be used to calculate the Synchronous Amplification Factor (SAF), a measure of the Quadrature Dynamic Stiffness of the rotor system. 0
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117
118
Data Plots
Equally important, IX and 2X plots, when combined with historical startup or shutdown data, can be used to detect changes in rotor system behavior that signal the presence of several types of malfunctions. 2X and higher-order polar and Bode plots can be used to identify the presence of, and changes in, resonances above running speed. Vibration in machines always involves a system, which includes the rotor shaft, the bearing supports, the machine casing, any attached piping and auxiliary equipment, and the floor and foundation structure. All of the components of the system will participate in every vibration mode of the system, but some parts of the system will vibrate in some modes more than others. Usually, we are interested in modes that involve large amounts of shaft vibration relative to the machine casing. These rotor modes are usually the most important for machinery management. Rotor resonances tend to produce large amplitudes of measured shaft relative vibration and are easily identified on Bode plots as a peak and on polar plots as a large loop (sometimes called a "polar circle"). However, it is important to remember that the machine casing, and consequently the observing probes, may also participate in the vibration. This is especially true for machines that have support structures with relatively low stiffness. Thus, the shaft relative vibration at resonances (and away from resonances, for that matter) may include significant contributions from casing vibration. This vibration can either add to or subtract from the shaft absolute vibration, depending on the phase relationship. Modes can be excited where most of the vibration occurs in the piping system, other attachments, or the foundation and support structure. These modes (sometimes called structural resonances) can produce a relatively low amplitude vibration of the casing, and, by extension, the measurement probes. These modes do not usually involve a significant amount of rotor motion, but, because of casing motion relative to the rotor shaft, they appear as small, but occasionally large, loops on polar plots. It can be difficult to tell the difference between a mode where the rotor and casing are both significantly vibrating in phase and a mode which produces a small vibration in the casing only. Both modes will produce small loops. Casing transducers, by revealing the amount of casing vibration, can help resolve the difference (see Chapter 12). A balance resonance has the following characteristics on a Bode plot: 1.
An amplitude peak, and
2.
A significant increase in phase lag at the frequency of the peak.
On a pobr plot. roto< ",,>oks wi.ll prod,..... Ia'll"' , Yin!l lo.""!'" or p;o.tiIII 10.-...... ()l lIn modn .. d l tnId toprOOUC1' ......u 1oops. ill IM opI it I>alana ..... .............. cau....d ~ an .ootI>,...- (u q ... U ... ppon ol,fJ...- t..... Ch.p4n IJ ~ ~ n ....... ~ ..... oft ""'"' vil.iblr on • polar plot btoc.uw tho:.oft.... arrnr'" a dootinctiw .....u loop. F'!lU"' 7· 5 ...,_ .• fI..d .. a nd polar plot. al. balaOCf' ncr and .. atlwr maII ..........ncn. A quod ri>rd< ...,.,.... thar: It..I X, comprn..t ....
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The Synchronous Amplification Factor is a measure of how much IX vibration is amplified when the system passes through a resonance. Systems with high effective damping tend to have a low SAF, and systems with low effective damping have a high SAF.There are four methods to measure the SAF (Figure 76), three using the Bode plot (Peak Ratio, Half-power Bandwidth, and Phase Slope), and one using the polar plot (a variation of the Half-power Bandwidth method). The SAF is based on the dynamic response of rotor systems to a rotating unbalance force. Because of this, all methods for measuring the SAF use l X, compensated polar and Bode plots. Slow roll compensation removes the part of the rotor response that is not due to unbalance. Because the Half-power Bandwidth method is so widely recognized, it will be described in detail here. See Appendix 4 for details of the other methods. The Half-power Bandwidth method [1], endorsed by the American Petroleum Institute, is also known in the USA as the API method. The term "half-power" originated in ac electrical circuit theory; the half-power point corresponds to the -3 dB (0.707) point of the voltage response curve of an electrical oscillator (because power is proportional to the square of the voltage, -6 dB (0.5) power corresponds to -3 dB (0.707) voltage). Since simple electrical and mechanical oscillators are mathematically equivalent, the method can be applied to rotor systems and yield meaningful results. The half-power points of the Bode plot, Dlol\' and D higlz in the figure, have a vibration amplitude of about 70% of the maximum balance resonance amplitude. Once these speeds have been identified, they define the half-power bandwidth (the blue region). These two rotor speeds (or frequencies) are used together with the speed of resonance, D res ' to calculate the SAF:
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ond mode appears to start at a point slightly away from the polar plot origin (where the color changes to red) , where it adds to the residual response of the first mode. The start point of the second mode can be mentally shifted to the origin of the plot. When this is done, the second mode heavy spot at this transducer location is approximately at the red dot. Polar plots from multiple planes of measurement can be used to estimate the different mode shapes of a machine. Figure 7-10 shows IX, compensated polar plots from vertical transducers in two planes. The measurement planes are both inside the nodal points associated with the bearings. In the polar plots, the first mode forms loops (blue) that have the same orientation in both planes. This data corresponds to the approximate mode shape in the top diagram. The shaft is bent into a simple curve, where each end of the shaft passes next to the measurement transducers at approximately the same time. Thus, this is called an in-phase mode. The second mode loops (red) have the opposite orientation; the high spots are 180 from each other. The shaft is bent into an "S" shape, where the shaft pa sses a transducer at one end half a turn later than at the other end. This mode shape is an example of an out-of-phase mode. Mode shape information like this is very important to balancing, because weight placement can influence several modes simultaneously or have little influence on a mode if the weight is close to a node. Knowledge of the mode shape is used to select the size of balancing weights and their axial distribution. Measurement of modes can be tricky. The measured amplitude of vibration is determined by the motion of the rotor and the motion of the measurement probe. lt is possible for the rotor and casing to have large amplitude, in-phase vibration in a particular mode, producing a relatively small shaft relative vibration signal that may look like a small system mode. Casing transducers can help identify such modes. The phase relationship of measured modes will also depend on the axial location of the measurement transducers relative to any nodal points in the rotor. In the first bowed mode at the top of the figure , if either transducer were moved to a location on the opposite side of the bearing, the nodal point near the bearing would produce a phase inversion in the polar plot. Knowledge of transducer axial location and the likely location of nodal points is important to establishing the correct mode shape of the rotor (see Chapter 12). Specific machine speed points on polar plots from different axial positions in the machine can be linked to produce a gross estimate of the mode shape of the rotor. This method is similar in concept to linking Keyphasor dots on multiple orbits to obtain the rotor deflection shape. However, unlike the orbit, polar 0
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plots only provide an approximation of the direction and amplitude of the response. An unfiltered orbit displays the actual dynamic position of a rotor. A polar plot, however, is created from filtered, no t unfiltered data, and it displays the vibration vector as measured by only one transducer. This produces a reasonably accurate estimate of the rotor position at the instant of the Keyphasor event if and only if the orbit is circular and predominantly IX. However, most I X orbits are elliptical, not circular. In the diagrams at the top of the figure, the pic tures represent an approximation to the shape of the rotor at the moment of the Keyphasor event. Finally, several possible mode shapes can have similar polar plots. We typ ically assume the simplest mode shape. Here , knowledge of the machine construction, together with knowledge of all lower modes, must be used to arrive at the probable mode shape. It is possible that the mode shape could be much more complicated. On a more complex machine, mode identification probes should be used to obtain more information at different planes in the machine, or existing measurements can be correlated with a good model of the rotor system. Polar and Bode plots are used to display startup and shutdown data. It is very useful to maintain a history of these plots so that data from different startups and shutdowns can be compared. Slow roll vectors, resonance frequencies , and the shape of the response should be checked for signs of change. Changes in resonance frequencies imply changes in rotor system support stiffness. Any change is evidence of a changing machine condition and should be investigated for its significance. APHTPlots Amplitude-PHase-Time (APHT) plots are a variation of polar and Bode plots. The vibration vectors are plotted versus time (trended) rather than versus frequency. These plots are designed to be used for long term trending of vibration vectors while the machine runs at constant speed. Because of the possibility that the slow roll vector will change, APHT plots are not normally slow roll compensated. APHT plots can display any harmonic of running speed. Vibration is a ratio of the applied force to the Dynamic Stiffness of the rotor system. Changes in vibration vectors mean that the applied force, the Dynamic Stiffness, or both have changed. Thus, a change in a vibration vector (either magnitude or direction) can provide early warning of a developing machine problem. Figure 7-11 shows a set of 2X APHT plots from a vertical reactor-coolant pump. The data was trended while the pump was running at 1187 rpm. For the first two months of operation, the 2X vibration did not change significantly. Then, in late October, the 2X amplitude and phase lag began to change. The
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Summary Polar, Bode, APHT, and acceptance region plots are designed to display vector data. Polar and Bode plots are used for startup and shutdown (transient) data, APHT plots are used primarily for long term trending during steady state conditions, and acceptance region plots are used to identify response outside of normal, expected behavior. Polar plots display the locus of a set of vibration vectors, in polar format. The phase angle is measured relative to the transducer location, in a direction opposite to rotation. Bode plots display the same information in two, separate, rectangular plots: phase lag versus frequency is plotted above, and vibration amplitude versus frequency below. APHT plots are similar to polar and Bode plots, except that the data is plotted against time instead of speed. Polar and Bode plots are usually slow roll compensated, using a slow roll vector selected from the slow roll speed range. The uncompensated Bode plot is used to identify the slow roll speed range and a suitable slow roll vector. Polar and Bode plots can be used to identify resonances and resonance frequencies. The Synchronous Amplification Factor (SAF) is most often measured from the Bode plot, although a polar plot can be used for this purpose. The IX polar plot is most often used to identify the location of the heavy spot for balancing purposes. Multiple plots from different axial locations can be used to identify the mode shapes for different resonances. APHT plots are used for long term monitoring of nX vibration vectors. The polar APHT plot can be used to define regions based on typical vibration vector behavior. Such a plot is called an acceptance region plot and can be used with software alarms to detect significant changes in vibration vectors. References 1. American Petroleum Institute, Tutorial on the API Standard Paragraphs Covering Rotor Dynamics and Balancing: An Introduction to Lateral Critical and Train Torsional Analysis and Rotor Balancing, API Publication 684 (Washington, D.C.: American Petroleum Institute, 1996), p. 3.
131
Chapter 8
Half and Full Spectrum Plots
IN
the use of filtering to obtain vibration vectors. These vectors are filtered to mul tiples of running speed, IX, 2X, 3X, etc., and provide us with information about the behavior of a machine at one of those particular frequencies. This information is presented to us as the amplitude and phase of the vibration at that frequency. However, machines can vibrate at many different frequencies simultaneously. These frequencies can be related or unrelated to running speed and include both sub synchronous and supersynchronous frequencies. Since these frequencies are associated with the operating condition of the machine, the machinery diagnostician must have some way to determine the frequency content of a vibration signal in order to make an accurate diagnosis. Vibration frequencies sometimes appear as a series of harmonics. The series consists of the lowest frequency in the series, called the fundamental, and a number of frequencies at integer multiples of the fundamental. In a typical series , the amplitude of higher order frequencies will decline rapidly. To avoid confusion , we will define a harmonic as any frequency that is an integer multiple of the fundamental. The first harmonic is the fundamental, the second harmonic has a frequency of twice the fundamental, the third harmonic has a frequency of three times the fundamental, etc. Often, the term harmonics will be used as a general term to indicate integer multiple frequencies that are above the fundamental. The fundamental vibration frequency of a serie s can be any vibration frequency. Often, the fundamental is IX, but it can also be any subsynchronous or supersynchronous frequency. For example, a series could be based on a fundamental at VlX and include IX, %X, 2X, %X, etc. In this serie s, the IhX is the funPREVIOUS CHAPTERS, WE HAVE DISCUSSED
132
Data Plots
damental or first harmonic. IX (= %X) is the second harmonic. % X is the third harmonic, etc. Such a series can be generated by a Y2X rub. as we will see later in this chapter. While the timebase and orbit can be used to evaluate frequency information. the most convenient plot for this purpose is the spectrum plot. The spectrum plot is created from the signal of a single transducer. It is the basic display of a spectrum analyzer and has been a mainstay of machinery diagnostics for many years. In the past few years. an important new tool, thefull spectrum plot, has been developed that uses the signals from a pair of orthogonal, shaft relative. vibration transducers. The full spectrum plot compares to a conventional spectrum plot in the same way that the orbit plot compares to a timebase plot. The full spectrum plot contains much more information than the spectrum plot. including vibration precession direction and orbit ellipticity. It is so important for machinery diagnostics that we now refer to the conventional spectrum plot as the halfspectrum plot. Spectrum plots are used to identity the frequency components that are present in complex vibration signals and to trend changes in the amplitude of frequency components. These frequencies include running speed. multiples of running speed. line frequency electrical noise, gear mesh frequencies, gear defect frequencies, rolling element bearing frequencies, and vane and blade pass frequencies. Rotor system natural frequencies that are excited will also show up on the spectrum plot. Subsynchronous frequencies that are often associated with fluid-induced instability, compressor rotating stall, compressor surge, or rub, and supersynchronous frequencies that are often associated with rubs and shaft cracks can also be identified. In this chapter, we will start with a discussion of the complex vibration signal, its frequency content. and how that information is displayed on the half spectrum plot. We will then discuss some technical aspects of spectrum signal processing, followed by the meaning of and the enhanced information content in the full spectrum plot. After full spectrum, we will discuss plot formats th at present spectrum data versus speed and versus time: the spectrum cascade plot for startup or shutdown data and the spectrum waterfall plot, which is used primarily for steady state trending. Both of these plots can be generated in half and full spectrum formats. These plots are similar in structure, but have important differences in their application.
The H.. 1f Spec tru m Plot 1.. 'l's ojart wil h a machin.. o p<'ral ing al .. ron~tant sf"'t'd_ Rt,.;au ,," o f a o "mbinal ion of u nbal an<:.. a nd o lh e. effects, t he mach ine i, vibrating in" com pli ca led " ."y. Th... un filt....ed ,i bration ~ignal f,o m a t ,a nsd ucer o n t bi, mac hin... will ....""a l all o f th.. complexity of the ma ch i.... ,ibral lOn Iha t fa lls wit bin th... b<>ndw;"llh o f I .... d ata collect ion sy~lem. A filtera1 signal from Ihi, t ra nsd lK't" will bo- a sine " .,,"" a l Ihe fill" , f"'"ll1<'ncy with son ,e a m plil ude a nd p h..... l'iflUre 8-1 sh...... a rom plex. u m..b<>... ,-;br.. lio n sig nal (mI) " 00,, M" i.... o f si.... wa'.....(bJu..) tha I add up to prod'Ke the t im""" 'l<' sig nal. U. ing the fo ur ,e, t ransfor m. th.. f""l....ncy. am plitu de. a nd pha,.., o f th""" sin... " ."..... (called com f'O""nls ) ca n bo-compu led from a d igital ...ml'le of the o rigina l ti meba... signal. The ph...... for ..""Ii sitlnal is meas u.-ed with lP1of"'Ct to th... Iri!'JI"" sillnal t hat
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Chapter 8
Half and Full Spectrum Plots
points and the limitations of digital sampling. The bottom plot shows the spectrum when a Hanning window is applied to the sample record. Note that the Y2X spectral line is narrower and higher, and the residual noise floor has virtually disappeared. A digitally calculated spectrum consists of discrete frequency bins, or lines, of finite width. The width of these lines, the resolution of the spectrum, is an important consideration. The maximum resolution of a spectrum is determined by the ratio of the spectrum span (the range of displayed frequencies) to the number ofspectrum lines that are displayed:
. Reso Iution =
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----"~---
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The spectrum plot is a collection of these lines, arranged side by side. The width of each line is equal to the resolution of the spectrum. For example, a 400 line spectrum with a span of zero to 200 Hz will have a resolution of . Resolution =
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Thus, each frequency line will, ideally, represent only the spectral energy in a 0.5 Hz (30 cpm) wide band from 0.25 Hz below to 0.25 Hz above the center frequency of the line. Accuracy in the displayed amplitude and frequency of a spectrum line will depend on where the actual vibration frequency is with respect to the center frequency and which window function is used. The limited resolution of spectrum plots means that there is always an uncertainty associated with any frequency we wish to measure. In the example above, a frequency actually located at, for example, 99.75 Hz, is displayed at 100 Hz. A spectrum plot with poor resolution will have a corresponding large uncertainty in the measured frequency. Even good resolution spectra may not be able to discriminate between vibration frequencies of exactly Y2X and 0.49X, an important distinction for malfunction diagnosis. Higher resolution (zoomed) spectra can help, but orbits with Keyphasor dots can sometimes be superior to spectrum plots for making this kind of discrimination (see Chapter 5). Noise can be a problem in spectrum plots. The Fourier transform of a spike is a series of spectrum lines extending to very high frequency. Thus, anything that produces a sharp corner in the signal will produce a series of spectrum lines. Sharp corners can result from shaft rebound at a rub contact point or from
137
138
Data Plots
an inadequate sampling frequency (causing a corner where a smooth transition really exists), among other things. Spikes or steps in the signal can originate from electrical noise problems or from scratches on the shaft. Spectrum plots are calculated from uncompensated waveforms, which may contain significant slow roll or glitch content. In general, the appearance of spectrum lines as a series of harmonics should be viewed with caution. Use timebase, orbit, or cascade plots (below) to validate the data. The Full Spectrum
The half spectrum is a spectrum of a single timebase waveform. The full spectrum is the spectrum ofan orbit. It is derived from the waveforms from two, orthogonal, shaft relative transducers, combined with knowledge of the direction of rotation. The information from the two transducers provides timing (phase) information that allows the full spectrum algorithm to determine the direction of precession at each frequency. Because the timing information is critical, the two waveforms must be sampled at the same time. The full spectrum is calculated by performing an FFT on each transducer waveform. The results are then subjected to another transform that converts the data into two new spectra that represent frequencies of precession, one spectrum for X to Y precession and one for Y to X precession. The last step uses the direction of rotation information to determine which of the spectra represents forward and which represents reverse precession frequencies. When this process is completed, the two spectra are combined into a single plot, the full spectrum plot (Figure 8-3). Figure 8-4 shows the relationships among timebase waveforms, half spectra, the orbit, and the full spectrum. The Y and X timebase waveforms and their half spectra are at the top. The two waveforms combine to produce the orbit at bottom left. The data used to generate the half spectra are further processed to produce the full spectrum at bottom right. Note that you cannot generate the full spectrum by combining the two half spectra. In the full spectrum plot, the spectrum of forward precession frequencies is on the positive horizontal axis and the spectrum of reverse precession frequencies is on the negative horizontal axis. Thus, for each frequency, there are two possible spectrum lines, one forward, and one reverse. The relative length of the spectrum lines for each frequency indicates the shape and direction of precession of the orbit filtered to that frequency. Figure 8-5 shows four, circular, IX orbits, with different directions of precession, indicated by the blank/dot sequence, and different directions of rotor rotation, indicated by the arrow. To the right of each orbit is its full spectrum. Since each orbit is circular, there is only one line, which is the peak-to-peak amplitude
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Figure 8-7 shows the progression of IX orbit shapes from forward circular through line to reverse circular and the associated full spectra. The relative size of the forward and reverse line heights correlate with the shape and precession direction of the orbit: 1. A single component, whether forward or reverse, means a circular
orbit. 2. The largest component determines the direction of precession. 3. The smaller the difference between the components, the more elliptical the orbit. 4. Equal components mean a line orbit. Complicated orbits will have forward and reverse components at many frequencies. Each pair of components represents a set of vectors that rotate in forward and reverse directions at a specific frequency. The most complex orbit can always be described by set of such vectors and full spectrum lines. The lines in the full spectrum represent the precessional structure of the orbit. Each pair of forward and reverse precession frequency components describes an orbital component, a suborbit (circular. elliptical, or line) with a particular precession frequency and direction. The entire orbit can be expressed as the sum of its orbital components in the same way that a timebase waveform can be expressed as the sum of its sine wave components. Figure 8-8 shows a complex orbit from a steam turbine with a Y2X rub. The orbit contains Y2X. IX, and some higher order vibration frequencies. The full spectrum helps clarify the complexity. Note that the IX spectral line pair shows that the IX component is largest. forward. and mildly elliptical. The ¥2X line pair shows that this component is nearly a line orbit. Also, there is 2X vibration present that is also a line orbit. Some small %X, the third harmonic of the YZX fundamental, is also visible. At first glance, the full spectrum might seem abstract. What is significant about pairs of vectors with forward and reverse precession? It lets us easily identify key orbit characteristics that might otherwise be obscured. Precession direction and ellipticity provide insight into the state of health of a machine. More importantly. some rotor system malfunctions can have characteristic signatures on a full spectrum plot that are not available on half spectrum plots. These char-
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Chapter 8
Half and Full Spectrum Plots
Summary The conventional, or half spectrum plot displays amplitude of vibration on th e vertical axis versus frequency of vibration on the horizontal axis. It is const ructed using the sampled timebase waveform from a single transducer. Spectrum plots can be used to identity the frequencies of running sp eed , harmonics of running speed. sub- and supersynchronous vibration frequencies, gear mesh frequencies, gear defect frequencies, rolling element bearing defect frequencies. vane and blade pass frequencies, sidebands. glitch. and line frequency noise. The full spectrum uses the waveforms from an orthogonal pair of vibration transducers (usually shaft relative). The full spectrum displays frequency and dire ction of precession on the horizontal axis. Forward precession frequencies are displayed to the right of the origin and reverse precession frequencie s are displayed to the left of the origin. The full spectru m is the spectrum of an orbit, and the forward and reverse frequency component pairs represent orbit components (filtered orbits). The ratio of the amplitudes of full spectrum component pairs gives information about the ellipticity and direction of precession of the components, important characteristics for malfunction diagnosis. However, there is no information about the orientation of the orbit. Spectrum cascade plots are sets of spectra that are collected during the startup or shutdown of a machine. Cascade plots can be constructed from either half spectra, or full spectra. Cascade plots have important information associated with vert ical, horizontal, and diagonal relationships. Waterfall plots are collections of spectra obtained. usually, during steady state operating conditions and plotted versus time. They also can use either half spectra or full spectra. The spectru m plot is a powerful tool when carefully applied. Because of its wide availability, there is a temptation to use the spectrum plot to the exclusion of other plot formats. But the spectrum, however powerful, is not a substitute for the information that can only be obtained in other plots: the filtered amplitude and phase in polar and Bode plots, the sh aft position information in average shaft centerline plots, the shape and frequency information in the orbit, and the waveform information in the timebase plot. All of this information is needed for comprehensive machinery management.
153
155
Chapter 9
Trend and XY Plots
IN
CHAPTER 7 WE DISCUSSED THE Amplitude-PHase-Time (APHT) plots, a form of polar and Bode plots, which are used for trending vector data. Besides vector data, there are many other parameters that we would like to trend: vibration levels, position data, process data, or any other parameter that can be useful for machine condition monitoring. The trend plot shows changes in and the rate of change of parameters that may signal a de veloping or impend ing problem in a machine. This information can be used to set limits or thresholds for action. We may also wish to examine how any of these variables change with respect to others. Such correlation is the heart of a good diagnostics methodology. No single variable or plot type can reveal everything about a machine. Data is usually correlated with many other pieces of information to arrive at a diagnosis. This kind of correlation is often done with multiple trend plots, where several variables are plotted against the same time scale. Correlation can also be done with XY plots, where two parameters are plotted against each other. This chapter will deal with the construction and uses of the trend plot and provide several examples. We will briefly discuss problems that can arise when the data sample rate is too low: Finally, we will discuss XYplots, a special type of trend plot used for correlation of two parameters.
Trend Plots The trend plot is a rectangular or polar plot on which the value of a measured parameter is plotted versus time. Trend plots can be used to display an y kind of data versus time: direct vibration, nX amplitude, nX phase (the API-IT plot is a trend plot that displays both), gap voltage (radial or thrust position),
156
Data Plots
rotor speed, and process variables, such as pressure, temperature, flow, or power. Trend plots are used to detect changes in these important parameters. They are used for both long and short term monitoring of machinery in all types of service and are, typically, an example of a steady state (constant speed) plot. The data for a trend plot can be collected by computer or by hand. Figure 91 shows a trend plot of hand-logged gap voltage from a fluid-film bearing at the discharge end of a refrigeration compressor. Due to improper grounding, electrostatic discharge gradually eroded 280 J.lm (11 mil) of the bearing, allowing the rotor shaft to slowly move into the babbitt. The trend plot alerted the operators to the fact that something was wrong, and they scheduled a shutdown in time to prevent serious damage. This is an example of how a very simple data set provided valuable information that saved the plant from an expensive failure. Even though a trend plot may look like a continuous history, it is not. Parameters are assumed to be slowly changing, so the data to be trended is sampled at intervals that depend on the importance of the machinery and the data. If a sudden change in behavior of the parameter occurs between samples, the data will be missed. Some data values may fluctuate periodically. For example, IX amplitude and phase may change periodically due to a thermal rub. The period of change for this kind of malfunction can be on the order of minutes to hours. Amplitude and phase modulation can occur in induction motors, due to uneven air gap, at twice the slip frequency; the period here is usually a fraction of a second. If the sampling frequency is less than twice the frequency of interest (does not satisfy the Nyquist criterion), then the frequency of the changes in the trend plot will be incorrect, an effect known as aliasing. The trend plot from an induction motor (Figure 9-2) looks like, at first glance, a timebase plot. However, it is a trend of unfiltered, peak-to-peak vibration that is changing periodically. This motor, which drives a boiler feed pump, has an uneven air gap problem. The vibration amplitude is modulated at a beat frequency equal to twice the slip frequency of the motor. The data in blue was sampled very rapidly, at about 10 samples per second. This produced a high resolution trend plot. The data in red is a portion of another trend plot from about two hours earlier, when the motor was experiencing the same problem. The sample rate was one sample every 10 seconds, a factor of 100 slower. Note that the frequency of the change of the red is much lower, the result of aliasing. This would not be obvious unless the data taken at the higher sample rate was available. The observed modulation frequency is much lower than the true modulation frequency. Parameters which change periodically like this are relatively rare, but this example demonstrates how the sample rate can produce a misleading picture of machine behavior. This effect can also happen
Chap ler 9
Trend . nd XV Plots
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